DAMAGE PREDICTION OF THE STEEL ARCH BRIDGE MODEL BASED ON ARTIFICIAL NEURAL NETWORK METHOD

被引:3
作者
Apriani, Widya [1 ]
Suryanita, Reni [2 ]
Firzal, Yohannes [3 ]
Lubis, Fadrizal [1 ]
机构
[1] Lancang Kuning Univ, Dept Civil Engn, Pekanbaru, Riau, Indonesia
[2] Univ Riau, Dept Civil Engn, Pekanbaru, Riau, Indonesia
[3] Univ Riau, Architecture Dept, Pekanbaru, Riau, Indonesia
来源
INTERNATIONAL JOURNAL OF GEOMATE | 2021年 / 20卷 / 82期
关键词
Artificial Neural Network; Damage Assessment; Damage Index; Reduction Stiffness; INDEX METHOD;
D O I
10.21660/2021.82.Gx293
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Failure in the advance prediction of bridge structure collapse requires an enormous cost of rehabilitation. In most cases, the projection of decreases or damage to the structure due to difficulty in the testing condition. Therefore, this study analyses the damage and identification of the critical structural components' severity on the steel girder arch bridge. Using the Artificial Neural Networks (ANNs), this research has tested a parametric steel girder arch bridge. The numerical model of the 146 supported girder has analysed by epoch 500 values of ANNSs's parameter. The stiffness of 10th element is assumed to drop 10%, 20%, 30%, and 40% of whole the tested. The architecture model of ANNs was three neurons in the input layer, five neurons in the hidden layer and one neuron in the output layer. The simulation of the data set were 90:10, 80:20, 70:30, and 50:50. ANNs shows the damage' severity in this the stiffness reduction tested by applying the damage index methods. In this research, the ANNs' simulation has been reliable to predict 98% for identifying structural damage. Thus, the results confirm the feasibility of the technique and its application in predicting structural failure.
引用
收藏
页码:46 / 52
页数:7
相关论文
共 16 条
[1]  
[Anonymous], 2014, Seismosignal - A computer program for signal processing of time-histories
[2]  
Apriani W, 2018, PENILAIAN JEMBATAN R, P18
[3]  
Apriani W, 2018, Program Studi Tek Sipil. Pertanian, V4, P103
[4]   The Evaluation of Changes in Camber Position to Deflection of Arch Steel Bridge in Extreme Loads [J].
Apriani, Widya ;
Lubis, Fadrizal ;
Suryanita, Reni ;
Firzal, Yohannes .
PROCEEDINGS OF AICCE'19: TRANSFORMING THE NATION FOR A SUSTAINABLE TOMORROW, 2020, 53 :843-853
[5]   The Comparison of Condition Evaluation of Siak II Steel Frame Bridge between the FCM Method and the Bridge Management System [J].
Apriani, Widya ;
Megasari, Shanti Wahyuni .
INTERNATIONAL CONFERENCE ON ENVIRONMENT AND TECHNOLOGY, 2020, 469
[6]  
Asmarani S, 2020, IOP C SER EARTH ENV, V426
[7]   ARTIFICIAL NEURAL NETWORK (ANN) MODELLING OF CONCRETE MIXED WITH WASTE CERAMIC TILES AND FLY ASH [J].
Elevado, Kenneth Jae T. ;
Galupino, Joenel G. ;
Gallardo, Ronaldo S. .
INTERNATIONAL JOURNAL OF GEOMATE, 2018, 15 (51) :154-159
[8]   Seismic damage assessment and prediction using artificial neural network of RC building considering irregularities [J].
Hait, Pritam ;
Sil, Arjun ;
Choudhury, Satyabrata .
JOURNAL OF STRUCTURAL INTEGRITY AND MAINTENANCE, 2020, 5 (01) :51-69
[9]  
Hakim SJS, 2013, STEEL COMPOS STRUCT, V14, P367
[10]  
Mardiyono M., 2012, TELKOMNIKA (Telecommun. Comput. Electron. Control), V10, P155, DOI [10.12928/telkomnika.v10i1.773, DOI 10.12928/TELKOMNIKA.V10I1.773]